The present exemplary embodiment relates to a method and system for implementing statistical process control (SPC) in a printing environment. It finds particular application in conjunction with print engines, and will be described with particular reference thereto. However, it is to be appreciated that the present exemplary embodiment is also amenable to other like applications.
By way of background, more sophisticated printing processes typically include process control sensors that accomplish a variety of tasks. As an example, process control sensors are typically positioned within a print engine of a printer to detect printed color. The printing system uses the data from the sensors to implement adjustments to the print engine to accommodate for errors or undesired variations in the printed color. This provides a useful feedback system that enhances the quality of the printing process.
At times, however, the process control sensor data may be in error and not representative of the true attribute being measured. This could occur for a variety of reasons, including an errant read. If the erroneous process control sensor reads are not identified as such, then actuator adjustments are made in response to the false reads. This unnecessarily induces color instability which leads to customer dissatisfaction.
As an example, a periodic “spike” in a tone reproduction curve sensor may induce a color shift in the printing process. The “spike” may merely be the result of a short-lived system problem such as electrical noise, as opposed to actual problems with the printed color. This undesired color shift will likely produce prints that truly are in error. Of course, these erroneous prints will then result in further unnecessary color shifting as a result of the process control sensor reads. It would be desirable to have error corrections made in the system only where necessary, i.e., where actual undesired color variations exist—and not as the result of an errant sensor read.
In addition, the notion of real time SPC (statistical process control) is extremely effective in determining process capability for a variety of industrial applications. It determines whether the process under analysis is stable and, if so, then determines the mean and variance of the process. If the process lacks stability, this is also typically detected. The lack of stability may indicate the presence of a problem, allowing for the commencement of a process by which a root cause may be pursued. If the process is stable, the estimates of the process mean and variation may still indicate a problem and also initiate a process by which the root cause can be pursued and eliminated, or if the estimates of the process mean and variation represent a system limitation, the system itself can be redesigned.
Real time SPC can be a strong pillar of any system of diagnosis, debugging, and/or process improvement for a manufacturing process. However, while SPC is capable of identifying the existence of a problem, it is limited in identifying root causes of problems. More intelligence in the system is necessary to do so. For example, in a printing environment, analyzing the frequency domain properties of a sensor signal may indicate an impending bearing failure. This type of intelligence would typically be used to supplement a conventional statistical process control (SPC) technique to allow for identification of such a root cause.
U.S. Pat. No. 5,053,815 (the '815 patent) relates to a reproduction apparatus having real time statistical process control. However, this patent teaches the concept of making comparisons to predetermine control limit reference values, as opposed to real time data streams indicative of whether a process is in stable control and/or, if it is in control, then determining the mean and degree of variation in the process. Moreover, the '815 patent does not disclose a technique for addressing errant reads of sensors. Indeed, it appears that errant sensor reads would simply be used in the process disclosed to predict incipient problems before failure occurs.